Summary of Constrained Multi-objective Optimization with Deep Reinforcement Learning Assisted Operator Selection, by Fei Ming and Wenyin Gong and Ling Wang and Yaochu Jin
Constrained Multi-objective Optimization with Deep Reinforcement Learning Assisted Operator Selection
by Fei Ming, Wenyin Gong, Ling Wang, Yaochu Jin
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to improving the performance of constrained multi-objective optimization evolutionary algorithms (CMOEAs) is proposed. By developing an online operator selection framework assisted by Deep Reinforcement Learning, the algorithm can adaptively select operators that maximize the improvement of the population according to the current state. This framework is embedded into four popular CMOEAs and tested on 42 benchmark problems, resulting in improved performance and versatility compared to nine state-of-the-art CMOEAs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve tricky optimization problems by using a special kind of machine learning called Deep Reinforcement Learning. It creates an online system that picks the best way to improve the problem solution based on how well it’s doing right now. This new approach is tested with four different ways of solving these kinds of problems and does better than nine other popular methods. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Reinforcement learning
